import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=13 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=2_24 ,  lowerCAmelCase__=30 ,  lowerCAmelCase__=4_00 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  )	->   Any:

							'''simple docstring'''


							__lowercase					     =				size if size is not None else {'''height''': 18, '''width''': 18}
							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				num_channels
							__lowercase					     =				image_size
							__lowercase					     =				min_resolution
							__lowercase					     =				max_resolution
							__lowercase					     =				do_resize
							__lowercase					     =				size
							__lowercase					     =				do_normalize
							__lowercase					     =				image_mean
							__lowercase					     =				image_std





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}




@require_torch
@require_vision
class   _UpperCamelCase (		_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Tuple     					=		ViTImageProcessor if is_vision_available() else None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				EfficientFormerImageProcessorTester(self		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							return self.image_proc_tester.prepare_image_processor_dict()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_mean'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_std'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_normalize'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_resize'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''size'''		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							pass





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							__lowercase					     =				prepare_image_inputs(self.image_proc_tester ,  equal_resolution=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  Image.Image		)

							# Test not batched input
							__lowercase					     =				image_processor(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processor(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							__lowercase					     =				prepare_image_inputs(self.image_proc_tester ,  equal_resolution=lowerCAmelCase__ ,  numpify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  np.ndarray		)

							# Test not batched input
							__lowercase					     =				image_processor(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processor(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							__lowercase					     =				prepare_image_inputs(self.image_proc_tester ,  equal_resolution=lowerCAmelCase__ ,  torchify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  torch.Tensor		)

							# Test not batched input
							__lowercase					     =				image_processor(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processor(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    ) ,  )
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()